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Open AccessArticle

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

1
King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
2
Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarq 13132, Jordan
3
College of Humanities and Sciences, University of Science and Technology of Fujairah, Fujairah 2202, UAE
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1745; https://doi.org/10.3390/app10051745
Received: 28 October 2019 / Revised: 20 February 2020 / Accepted: 25 February 2020 / Published: 3 March 2020
(This article belongs to the Section Computing and Artificial Intelligence)
Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers. View Full-Text
Keywords: software defect prediction; ensembles; clustering; segmentation; classification software defect prediction; ensembles; clustering; segmentation; classification
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MDPI and ACS Style

Alsawalqah, H.; Hijazi, N.; Eshtay, M.; Faris, H.; Radaideh, A.A.; Aljarah, I.; Alshamaileh, Y. Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns. Appl. Sci. 2020, 10, 1745. https://doi.org/10.3390/app10051745

AMA Style

Alsawalqah H, Hijazi N, Eshtay M, Faris H, Radaideh AA, Aljarah I, Alshamaileh Y. Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns. Applied Sciences. 2020; 10(5):1745. https://doi.org/10.3390/app10051745

Chicago/Turabian Style

Alsawalqah, Hamad; Hijazi, Neveen; Eshtay, Mohammed; Faris, Hossam; Radaideh, Ahmed A.; Aljarah, Ibrahim; Alshamaileh, Yazan. 2020. "Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns" Appl. Sci. 10, no. 5: 1745. https://doi.org/10.3390/app10051745

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